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Distributed Data Mining in a Ubiquitous Healthcare Framework

  • Conference paper
Advances in Artificial Intelligence (Canadian AI 2007)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4509))

Abstract

Ubiquitous Healthcare (u-healthcare) which focuses on automated applications that can provide healthcare to citizens anywhere/anytime using wired and wireless mobile technologies is becoming increasingly important. Ubiquitous healthcare data provides a mine of hidden knowledge which can be exploited in preventive care and “wellness” recommendations. Data mining is therefore a significant aspect of such systems. Distributed Data mining (DDM) techniques for knowledge discovery from databases help in the thorough analysis of data collected from healthcare facilities enabling efficient decision-making and strategic planning. This paper presents and discusses the development of a prototype ubiquitous healthcare system. The prospects for integrating data mining into this framework are studied using a distributed data mining system. The DDM system employs a mixture modelling mechanism for data partitioning. Initial results with some standard medical databases offer a plausible outlook for future integration.

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Authors and Affiliations

  1. Carnegie Mellon University - Australia, H. John Heinz III School of Public Policy and Management, Adelaide, SA, Australia

    Murlikrishna Viswanathan

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  1. Murlikrishna Viswanathan
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Ziad Kobti Dan Wu

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© 2007 Springer Berlin Heidelberg

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Viswanathan, M. (2007). Distributed Data Mining in a Ubiquitous Healthcare Framework. In: Kobti, Z., Wu, D. (eds) Advances in Artificial Intelligence. Canadian AI 2007. Lecture Notes in Computer Science(), vol 4509. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72665-4_23

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  • DOI: https://doi.org/10.1007/978-3-540-72665-4_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-72664-7

  • Online ISBN: 978-3-540-72665-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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